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Jon Timmis - One of the best experts on this subject based on the ideXlab platform.
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special issue on Artificial Immune Systems
Swarm Intelligence, 2010Co-Authors: Jon Timmis, Paul S Andrews, Emma HartAbstract:The field of Artificial Immune Systems (AIS) is a diverse area of research that bridges the disciplines of immunology and engineering. AIS algorithms are typically developed from the abstraction of Immune system theories, processes and agents, and they have been applied to a wide variety of engineering applications including computer security, fault tolerance, data mining and optimisation. More recently there has been a growing trend within AIS to facilitate closer interaction between the domains of immunology and engineering through the use of various mathematical and computational modelling approaches. These have included dynamical Systems analysis, agent-based modelling and cellular automata. The resulting models serve a dual purpose: to improve understanding of the biological domain, and to aid the development of more biologically inspired AIS for engineering problems. The field of swarm intelligence (SI) encompasses a wide range of scientific and engineering disciplines to explore and exploit the complex behaviours that arise from groupings of agents such as social insects or animals. Research in this field incorporates many decentralised and distributed Systems that exploit the collective behaviour that emerges from the interaction of individual agents with each other and their environment. This perspective affords a natural link between SI and AIS: many Immune algorithms operate in a very similar manner with populations of Immune agents exhibiting similar high-level collective behaviours; it has furthermore been suggested by several authors that the natural Immune
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theoretical advances in Artificial Immune Systems
Theoretical Computer Science, 2008Co-Authors: Jon Timmis, Andrew N W Hone, Thomas Stibor, Edward B ClarkAbstract:Artificial Immune Systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural Immune system, in particular the theories of clonal selection, Immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, Immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.
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revisiting the foundations of Artificial Immune Systems for data mining
IEEE Transactions on Evolutionary Computation, 2007Co-Authors: Alex A Freitas, Jon TimmisAbstract:This paper advocates a problem-oriented approach for the design of Artificial Immune Systems (AIS) for data mining. By problem-oriented approach we mean that, in real-world data mining applications the design of an AIS should take into account the characteristics of the data to be mined together with the application domain: the components of the AIS - such as its representation, affinity function, and Immune process - should be tailored for the data and the application. This is in contrast with the majority of the literature, where a very generic AIS algorithm for data mining is developed and there is little or no concern in tailoring the components of the AIS for the data to be mined or the application domain. To support this problem-oriented approach, we provide an extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection. We discuss several important lessons to be taken from the natural Immune system to design new AIS that are considerably more adaptive than current AIS. Finally, we conclude this paper with a summary of seven limitations of current AIS for data mining and ten suggested research directions.
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Artificial Immune Systems—today and tomorrow
Natural Computing, 2007Co-Authors: Jon TimmisAbstract:In this position paper, we argue that the field of Artificial Immune Systems (AIS) has reached an impasse. For many years, Immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive Immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.
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Artificial Immune Systems today and tomorrow
Natural Computing, 2007Co-Authors: Jon TimmisAbstract:In this position paper, we argue that the field of Artificial Immune Systems (AIS) has reached an impasse. For many years, Immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive Immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.
Melanie E Moses - One of the best experts on this subject based on the ideXlab platform.
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scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
Swarm Intelligence, 2010Co-Authors: Soumya Banerjee, Melanie E MosesAbstract:Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed Systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.
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scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
arXiv: Quantitative Methods, 2010Co-Authors: Soumya Banerjee, Melanie E MosesAbstract:Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed Systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed Systems AIS applications.
Soumya Banerjee - One of the best experts on this subject based on the ideXlab platform.
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scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
Swarm Intelligence, 2010Co-Authors: Soumya Banerjee, Melanie E MosesAbstract:Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed Systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.
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scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
arXiv: Quantitative Methods, 2010Co-Authors: Soumya Banerjee, Melanie E MosesAbstract:Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed Systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed Systems AIS applications.
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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sub pixel mapping based on Artificial Immune Systems for remote sensing imagery
Pattern Recognition, 2013Co-Authors: Yanfei Zhong, Liangpei ZhangAbstract:A new sub-pixel mapping strategy inspired by the clonal selection theory in Artificial Immune Systems (AIS), namely, the clonal selection sub-pixel mapping (CSSM) framework, is proposed for the sub-pixel mapping of remote sensing imagery, to provide detailed information on the spatial distribution of land cover within a mixed pixel. In CSSM, the sub-pixel mapping problem becomes one of assigning land-cover classes to the sub-pixels while maximizing the spatial dependence by the clonal selection algorithm. Each antibody in CSSM represents a possible sub-pixel configuration of the pixel. CSSM evolves the antibody population by inheriting the biological properties of human Immune Systems, i.e., cloning, mutation, and memory, to build a memory cell population with a diverse set of locally optimal solutions. Based on the memory cell population, CSSM outputs the value of the memory cell and finds the optimal sub-pixel mapping result. Based on the framework of CSSM, three sub-pixel mapping algorithms with different mutation operators, namely, the clonal selection sub-pixel mapping algorithm based on Gaussian mutation (G-CSSM), Cauchy mutation (C-CSSM), and non-uniform mutation (N-CSSM), have been developed. They each have a similar sub-pixel mapping process, except for the mutation processes, which use different mutation operators. The proposed algorithms are compared with the following sub-pixel mapping algorithms: direct neighboring sub-pixel mapping (DNSM), the sub-pixel mapping algorithm based on spatial attraction models (SASM), the BP neural network sub-pixel mapping algorithm (BPSM), and the sub-pixel mapping algorithm based on a genetic algorithm (GASM), using both synthetic images (Artificial and degraded synthetic images) and real remote sensing imagery. The experimental results demonstrate that the proposed approaches outperform the previous sub-pixel mapping algorithms, and hence provide an effective option for the sub-pixel mapping of remote sensing imagery.
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A sub-pixel mapping algorithm based on Artificial Immune Systems for remote sensing imagery
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009Co-Authors: Yanfei Zhong, Liangpei Zhang, Li Pingxiang, Huanfeng ShenAbstract:In this paper, a new sub-pixel mapping method inspired by the clonal selection algorithm (CSA) in Artificial Immune Systems (AIS) is proposed, namely clonal selection subpixel mapping (CSSM). In CSSM, the sub-pixel mapping problem becomes one of assigning land cover classes to the sub-pixels while maximizing the spatial dependence by clonal selection algorithm. CSSM inherits the biologic properties of human Immune Systems, i.e. clone, mutation, memory, to build a memory-cell population with a diverse set of local optimal solutions. Based on the memory-cell population, CSSM outputs the value of the memory cell and find the optimal sub-pixel mapping result. The proposed method was tested using the synthetic and degraded real imagery. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of remote sensing imagery.
Uwe Aickelin - One of the best experts on this subject based on the ideXlab platform.
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Artificial Immune Systems (INTROS 2).
arXiv: Neural and Evolutionary Computing, 2013Co-Authors: Uwe Aickelin, Dipankar Dasgupta, Feng GuAbstract:The biological Immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or non-self substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the Immune system. The innate Immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive Immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the Immune system have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the Immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.
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Artificial Immune Systems 2010
arXiv: Artificial Intelligence, 2010Co-Authors: Julie Greensmith, Amanda Whitbrook, Uwe AickelinAbstract:The human Immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified Immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the Immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the Immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.
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detecting anomalous process behaviour using second generation Artificial Immune Systems
arXiv: Artificial Intelligence, 2010Co-Authors: Jamie Twycross, Uwe Aickelin, Amanda WhitbrookAbstract:Artificial Immune Systems have been successfully applied to a number of problem domains including fault tolerance and data mining, but have been shown to scale poorly when applied to computer intrusion detec- tion despite the fact that the biological Immune system is a very effective anomaly detector. This may be because AIS algorithms have previously been based on the adaptive Immune system and biologically-naive mod- els. This paper focuses on describing and testing a more complex and biologically-authentic AIS model, inspired by the interactions between the innate and adaptive Immune Systems. Its performance on a realistic process anomaly detection problem is shown to be better than standard AIS methods (negative-selection), policy-based anomaly detection methods (systrace), and an alternative innate AIS approach (the DCA). In addition, it is shown that runtime information can be used in combination with system call information to enhance detection capability.
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biological inspiration for Artificial Immune Systems
arXiv: Artificial Intelligence, 2010Co-Authors: Jamie Twycross, Uwe AickelinAbstract:Artificial Immune Systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these Systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate Immune Systems, and that AISs should employ systemic models of the Immune system to structure their overall design. An outline of plant and invertebrate Immune Systems is presented, and a number of contemporary research that more biologically-realistic AISs could have is also discussed.
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'Artificial Immune Systems Tutorial'
A Bibliography COMPUTER SCIENCE DEPARTMENT THE UNIVERSITY OF MEMPHIS USA, 2010Co-Authors: Uwe Aickelin, Dipankar DasguptaAbstract:The biological Immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the Immune system. The innate Immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive Immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the Immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the Immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.
